Mining TCGA data using Boolean implications.
Mining TCGA data using Boolean implications.
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DOI：
10.1371/journal.pone.0102119
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Dill DL
中科院分区：
文献类型：
--
作者：
Sinha S;Tsang EK;Zeng H;Meister M;Dill DL
布尔蕴涵（if-then规则）提供了一种概念上简单、统一和高度可扩展的方法来找到随机变量对之间的关联。在本文中，我们建议使用布尔含义，以找到不同的数据类型（突变，拷贝数改变，DNA甲基化和基因表达）从胶质母细胞瘤（GBM）和卵巢浆液性囊腺瘤（OV）的数据集之间的关系癌症基因组图谱（TCGA）。我们从这些数据集中发现了成千上万的布尔含义。通过对布尔蕴涵发现的关系和常用的关联挖掘方法发现的关系的直接比较表明，现有的方法会遗漏布尔蕴涵发现的关系。此外，布尔蕴涵揭示的许多关系反映了癌症生物学的重要方面。我们发现的例子包括拷贝数改变，DNA甲基化和基因表达之间的顺式关系，一个新的突变层次和复发的拷贝数改变，众所周知的肿瘤抑制基因的杂合性丢失，以及与GBM中IDH 1突变相关的超甲基化表型。本文中使用的布尔蕴涵结果可以在http://crookneck.stanford.edu/microarray/TCGANetworks/上访问。
Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/.
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